Name
Title | ||
|---|---|---|
EEG signals classification using the K-means clustering and a multilayer perceptron neural network model |
Abstract | ||
|---|---|---|
We introduced a multilayer perceptron neural network (MLPNN) based classification model as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were decomposed into frequency sub-bands using discrete wavelet transform (DWT). The wavelet coefficients were clustered using the K-means algorithm for each frequency sub-band. The probability distributions were computed according to distribution of wavelet coefficients to the clusters, and then used as inputs to the MLPNN model. We conducted five different experiments to evaluate the performance of the proposed model in the classifications of different mixtures of healthy segments, epileptic seizure free segments and epileptic seizure segments. We showed that the proposed model resulted in satisfactory classification accuracy rates. (C) 2011 Elsevier Ltd. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.eswa.2011.04.149 | EXPERT SYSTEMS WITH APPLICATIONS |
Keywords | Field | DocType |
Epilepsy,K-means clustering,Discrete wavelet transform (DWT),Multilayer perceptron neural network (MLPNN),EEC signals,Classification | k-means clustering,Pattern recognition,Computer science,Multilayer perceptron neural network,Epileptic seizure,Probability distribution,Artificial intelligence,Discrete wavelet transform,Electroencephalography,Machine learning,Wavelet | Journal |
Volume | Issue | ISSN |
38 | 10 | 0957-4174 |
Citations | PageRank | References |
33 | 1.46 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Umut Orhan | 1 | 60 | 8.66 |
| Mahmut Hekim | 2 | 43 | 3.89 |
| Mahmut Ozer | 3 | 71 | 9.68 |